Overview

Dataset statistics

Number of variables15
Number of observations494
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory52.2 KiB
Average record size in memory108.1 B

Variable types

NUM12
CAT3

Warnings

Symbol has constant value "494" Constant
Series has constant value "494" Constant
Open Price is highly correlated with Prev Close and 5 other fieldsHigh correlation
Prev Close is highly correlated with Open Price and 5 other fieldsHigh correlation
High Price is highly correlated with Prev Close and 5 other fieldsHigh correlation
Low Price is highly correlated with Prev Close and 5 other fieldsHigh correlation
Last Price is highly correlated with Prev Close and 5 other fieldsHigh correlation
Close Price is highly correlated with Prev Close and 5 other fieldsHigh correlation
Average Price is highly correlated with Prev Close and 5 other fieldsHigh correlation
No. of Trades is highly correlated with Total Traded Quantity and 1 other fieldsHigh correlation
Total Traded Quantity is highly correlated with No. of TradesHigh correlation
Turnover is highly correlated with No. of TradesHigh correlation
Date has unique values Unique
Total Traded Quantity has unique values Unique
Turnover has unique values Unique
No. of Trades has unique values Unique
Deliverable Qty has unique values Unique

Reproduction

Analysis started2020-09-12 09:00:42.868110
Analysis finished2020-09-12 09:01:45.296644
Duration1 minute and 2.43 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Symbol
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
RCOM
494 
ValueCountFrequency (%) 
RCOM494100.0%
 
2020-09-12T14:31:45.611696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-12T14:31:45.845499image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:46.087852image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

Series
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
EQ
494 
ValueCountFrequency (%) 
EQ494100.0%
 
2020-09-12T14:31:46.523846image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-12T14:31:46.748029image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:46.993344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Date
Categorical

UNIQUE

Distinct494
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
01-Aug-2017
 
1
07-Dec-2018
 
1
24-May-2017
 
1
05-Feb-2018
 
1
05-Jul-2017
 
1
Other values (489)
489 
ValueCountFrequency (%) 
01-Aug-201710.2%
 
07-Dec-201810.2%
 
24-May-201710.2%
 
05-Feb-201810.2%
 
05-Jul-201710.2%
 
16-Aug-201710.2%
 
27-Sep-201810.2%
 
01-Feb-201910.2%
 
15-Jun-201810.2%
 
17-Jan-201810.2%
 
Other values (484)48498.0%
 
2020-09-12T14:31:47.320605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique494 ?
Unique (%)100.0%
2020-09-12T14:31:47.631545image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length11
Mean length11
Min length11

Prev Close
Real number (ℝ≥0)

HIGH CORRELATION

Distinct314
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.8201417
Minimum1.85
Maximum36.15
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2020-09-12T14:31:47.956410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.85
5-th percentile3.8975
Q112.9125
median15.925
Q322.2875
95-th percentile29.5025
Maximum36.15
Range34.3
Interquartile range (IQR)9.375

Descriptive statistics

Standard deviation7.322269665
Coefficient of variation (CV)0.435327466
Kurtosis-0.2555729794
Mean16.8201417
Median Absolute Deviation (MAD)4.275
Skewness0.0955154412
Sum8309.15
Variance53.61563304
MonotocityNot monotonic
2020-09-12T14:31:48.322850image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
13.3561.2%
 
13.5561.2%
 
15.7561.2%
 
13.8561.2%
 
19.0551.0%
 
13.551.0%
 
11.6551.0%
 
13.840.8%
 
23.840.8%
 
14.140.8%
 
Other values (304)44389.7%
 
ValueCountFrequency (%) 
1.8510.2%
 
1.920.4%
 
1.9510.2%
 
220.4%
 
2.120.4%
 
ValueCountFrequency (%) 
36.1510.2%
 
35.3510.2%
 
3410.2%
 
33.910.2%
 
33.310.2%
 

Open Price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct309
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.86325911
Minimum1.85
Maximum37.25
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2020-09-12T14:31:48.676744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.85
5-th percentile3.78
Q112.9125
median16
Q322.3375
95-th percentile29.5375
Maximum37.25
Range35.4
Interquartile range (IQR)9.425

Descriptive statistics

Standard deviation7.368338006
Coefficient of variation (CV)0.4369462604
Kurtosis-0.2194136014
Mean16.86325911
Median Absolute Deviation (MAD)4.3
Skewness0.1057878562
Sum8330.45
Variance54.29240498
MonotocityNot monotonic
2020-09-12T14:31:49.020419image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
13.791.8%
 
13.461.2%
 
13.361.2%
 
13.851.0%
 
13.9551.0%
 
13.651.0%
 
2540.8%
 
1240.8%
 
13.940.8%
 
13.540.8%
 
Other values (299)44289.5%
 
ValueCountFrequency (%) 
1.8510.2%
 
1.920.4%
 
1.9510.2%
 
230.6%
 
2.120.4%
 
ValueCountFrequency (%) 
37.2510.2%
 
35.2510.2%
 
34.610.2%
 
34.3510.2%
 
33.9510.2%
 

High Price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct305
Distinct (%)61.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.44301619
Minimum1.85
Maximum40.15
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2020-09-12T14:31:49.359090image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.85
5-th percentile3.83
Q113.3625
median16.575
Q322.9
95-th percentile30.8525
Maximum40.15
Range38.3
Interquartile range (IQR)9.5375

Descriptive statistics

Standard deviation7.612551476
Coefficient of variation (CV)0.436424033
Kurtosis-0.1577560441
Mean17.44301619
Median Absolute Deviation (MAD)4.475
Skewness0.1090430051
Sum8616.85
Variance57.95093997
MonotocityNot monotonic
2020-09-12T14:31:49.718096image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
12.181.6%
 
1761.2%
 
13.551.0%
 
23.551.0%
 
13.9551.0%
 
13.751.0%
 
14.251.0%
 
16.140.8%
 
13.940.8%
 
16.340.8%
 
Other values (295)44389.7%
 
ValueCountFrequency (%) 
1.8510.2%
 
1.910.2%
 
1.9510.2%
 
220.4%
 
2.0510.2%
 
ValueCountFrequency (%) 
40.1510.2%
 
37.4510.2%
 
36.1510.2%
 
35.4510.2%
 
34.910.2%
 

Low Price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct292
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.2257085
Minimum1.85
Maximum33.95
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2020-09-12T14:31:50.058658image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.85
5-th percentile3.7475
Q112.3125
median15.45
Q321.65
95-th percentile28.2025
Maximum33.95
Range32.1
Interquartile range (IQR)9.3375

Descriptive statistics

Standard deviation7.105001881
Coefficient of variation (CV)0.4378854631
Kurtosis-0.2873532686
Mean16.2257085
Median Absolute Deviation (MAD)4.2
Skewness0.08697925847
Sum8015.5
Variance50.48105173
MonotocityNot monotonic
2020-09-12T14:31:50.404508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
13.6581.6%
 
13.7571.4%
 
13.3561.2%
 
16.7561.2%
 
13.4561.2%
 
22.6551.0%
 
2251.0%
 
22.140.8%
 
11.540.8%
 
11.6540.8%
 
Other values (282)43988.9%
 
ValueCountFrequency (%) 
1.8510.2%
 
1.920.4%
 
1.9510.2%
 
230.6%
 
2.120.4%
 
ValueCountFrequency (%) 
33.9510.2%
 
33.610.2%
 
33.3510.2%
 
33.0510.2%
 
32.710.2%
 

Last Price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct308
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.77732794
Minimum1.85
Maximum36.3
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2020-09-12T14:31:50.753404image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.85
5-th percentile3.7475
Q112.9125
median15.85
Q322.3375
95-th percentile29.2025
Maximum36.3
Range34.45
Interquartile range (IQR)9.425

Descriptive statistics

Standard deviation7.312128505
Coefficient of variation (CV)0.4358339143
Kurtosis-0.2498493279
Mean16.77732794
Median Absolute Deviation (MAD)4.275
Skewness0.08170976461
Sum8288
Variance53.46722327
MonotocityNot monotonic
2020-09-12T14:31:51.097312image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
13.981.6%
 
13.5561.2%
 
13.651.0%
 
13.6551.0%
 
2351.0%
 
15.8551.0%
 
11.651.0%
 
14.451.0%
 
13.9540.8%
 
16.8540.8%
 
Other values (298)44289.5%
 
ValueCountFrequency (%) 
1.8510.2%
 
1.920.4%
 
1.9510.2%
 
220.4%
 
2.120.4%
 
ValueCountFrequency (%) 
36.310.2%
 
35.110.2%
 
33.9510.2%
 
33.910.2%
 
33.8510.2%
 

Close Price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct313
Distinct (%)63.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.75910931
Minimum1.85
Maximum36.15
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2020-09-12T14:31:51.549490image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.85
5-th percentile3.7475
Q112.8625
median15.9
Q322.2375
95-th percentile29.45
Maximum36.15
Range34.3
Interquartile range (IQR)9.375

Descriptive statistics

Standard deviation7.317378936
Coefficient of variation (CV)0.4366209922
Kurtosis-0.2566461148
Mean16.75910931
Median Absolute Deviation (MAD)4.25
Skewness0.08507045313
Sum8279
Variance53.5440345
MonotocityNot monotonic
2020-09-12T14:31:51.905415image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
13.5561.2%
 
15.7561.2%
 
13.8561.2%
 
13.3561.2%
 
19.0551.0%
 
13.551.0%
 
11.6551.0%
 
13.740.8%
 
14.140.8%
 
15.6540.8%
 
Other values (303)44389.7%
 
ValueCountFrequency (%) 
1.8510.2%
 
1.920.4%
 
1.9510.2%
 
220.4%
 
2.120.4%
 
ValueCountFrequency (%) 
36.1510.2%
 
35.3510.2%
 
3410.2%
 
33.910.2%
 
33.310.2%
 

Average Price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct448
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.8215587
Minimum1.85
Maximum36.58
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2020-09-12T14:31:52.236914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.85
5-th percentile3.767
Q112.87
median15.91
Q322.4125
95-th percentile29.657
Maximum36.58
Range34.73
Interquartile range (IQR)9.5425

Descriptive statistics

Standard deviation7.355213561
Coefficient of variation (CV)0.4372492282
Kurtosis-0.2427677812
Mean16.8215587
Median Absolute Deviation (MAD)4.255
Skewness0.09294748374
Sum8309.85
Variance54.09916653
MonotocityNot monotonic
2020-09-12T14:31:52.576769image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
12.8730.6%
 
2.430.6%
 
13.8330.6%
 
14.1320.4%
 
13.8420.4%
 
28.8220.4%
 
13.7120.4%
 
22.5220.4%
 
21.2920.4%
 
5.3220.4%
 
Other values (438)47195.3%
 
ValueCountFrequency (%) 
1.8510.2%
 
1.910.2%
 
1.9210.2%
 
1.9510.2%
 
220.4%
 
ValueCountFrequency (%) 
36.5810.2%
 
35.6810.2%
 
34.8710.2%
 
34.0110.2%
 
33.3410.2%
 

Total Traded Quantity
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct494
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81814773.31
Minimum2367025
Maximum827714011
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2020-09-12T14:31:52.925192image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2367025
5-th percentile8931096.25
Q132475402
median56953703
Q393166704.5
95-th percentile247548615.7
Maximum827714011
Range825346986
Interquartile range (IQR)60691302.5

Descriptive statistics

Standard deviation96977318.89
Coefficient of variation (CV)1.185327722
Kurtosis22.3314008
Mean81814773.31
Median Absolute Deviation (MAD)29261214.5
Skewness4.053221929
Sum4.041649801e+10
Variance9.404600378e+15
MonotocityNot monotonic
2020-09-12T14:31:53.269499image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20113510210.2%
 
10028515410.2%
 
9264056210.2%
 
5575813210.2%
 
2486405910.2%
 
1783839610.2%
 
26493032510.2%
 
1904161110.2%
 
1552212410.2%
 
4518536010.2%
 
Other values (484)48498.0%
 
ValueCountFrequency (%) 
236702510.2%
 
237716410.2%
 
261749410.2%
 
302718010.2%
 
358459210.2%
 
ValueCountFrequency (%) 
82771401110.2%
 
78183647810.2%
 
77465348510.2%
 
68528668910.2%
 
55684906810.2%
 

Turnover
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct494
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1489669927
Minimum4734050
Maximum2.506661893e+10
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2020-09-12T14:31:53.599696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum4734050
5-th percentile99812740.12
Q1465384016.9
median800408584.7
Q31597793883
95-th percentile4326567372
Maximum2.506661893e+10
Range2.506188488e+10
Interquartile range (IQR)1132409866

Descriptive statistics

Standard deviation2484907442
Coefficient of variation (CV)1.66809264
Kurtosis44.63801603
Mean1489669927
Median Absolute Deviation (MAD)433619202.1
Skewness5.874509422
Sum7.358969441e+11
Variance6.174764993e+18
MonotocityNot monotonic
2020-09-12T14:31:53.965767image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
300626870.410.2%
 
79712624410.2%
 
122830695710.2%
 
352217019.110.2%
 
927354937.410.2%
 
666200143.810.2%
 
98872774.510.2%
 
135348513810.2%
 
381476210.710.2%
 
158213237710.2%
 
Other values (484)48498.0%
 
ValueCountFrequency (%) 
473405010.2%
 
4992044.410.2%
 
5104113.310.2%
 
665979610.2%
 
8603020.810.2%
 
ValueCountFrequency (%) 
2.506661893e+1010.2%
 
2.503625078e+1010.2%
 
2.146104751e+1010.2%
 
1.475117543e+1010.2%
 
1.423465799e+1010.2%
 

No. of Trades
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct494
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56062.12146
Minimum1408
Maximum648577
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2020-09-12T14:31:54.299291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1408
5-th percentile7206.65
Q121688
median38198
Q361075.5
95-th percentile145938.65
Maximum648577
Range647169
Interquartile range (IQR)39387.5

Descriptive statistics

Standard deviation71264.93347
Coefficient of variation (CV)1.271177965
Kurtosis31.96400336
Mean56062.12146
Median Absolute Deviation (MAD)18386.5
Skewness4.885417847
Sum27694688
Variance5078690743
MonotocityNot monotonic
2020-09-12T14:31:54.639300image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3379010.2%
 
2285810.2%
 
5051010.2%
 
3924910.2%
 
1979610.2%
 
5870910.2%
 
341810.2%
 
1434610.2%
 
393310.2%
 
3414510.2%
 
Other values (484)48498.0%
 
ValueCountFrequency (%) 
140810.2%
 
152710.2%
 
156010.2%
 
238510.2%
 
279110.2%
 
ValueCountFrequency (%) 
64857710.2%
 
63923110.2%
 
59196910.2%
 
58630710.2%
 
34922710.2%
 

Deliverable Qty
Real number (ℝ≥0)

UNIQUE

Distinct494
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11703466.1
Minimum1166757
Maximum81671630
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2020-09-12T14:31:54.999269image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1166757
5-th percentile2611888.85
Q15204915
median8018975.5
Q312782847
95-th percentile33890206.4
Maximum81671630
Range80504873
Interquartile range (IQR)7577932

Descriptive statistics

Standard deviation11563782.37
Coefficient of variation (CV)0.9880647556
Kurtosis10.97018548
Mean11703466.1
Median Absolute Deviation (MAD)3349606
Skewness2.96949147
Sum5781512251
Variance1.337210626e+14
MonotocityNot monotonic
2020-09-12T14:31:55.357968image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
816885810.2%
 
520022010.2%
 
657955210.2%
 
692976210.2%
 
677923510.2%
 
702172610.2%
 
912129810.2%
 
631332010.2%
 
2686705010.2%
 
1378647610.2%
 
Other values (484)48498.0%
 
ValueCountFrequency (%) 
116675710.2%
 
122039610.2%
 
137683610.2%
 
139981510.2%
 
144243710.2%
 
ValueCountFrequency (%) 
8167163010.2%
 
7883038810.2%
 
7013506810.2%
 
7012885310.2%
 
6930596110.2%
 

% Dly Qt to Traded Qty
Real number (ℝ≥0)

Distinct443
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.64740891
Minimum5.37
Maximum100
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2020-09-12T14:31:55.831946image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum5.37
5-th percentile8.1095
Q110.8625
median14.075
Q320.2475
95-th percentile69.926
Maximum100
Range94.63
Interquartile range (IQR)9.385

Descriptive statistics

Standard deviation19.82243103
Coefficient of variation (CV)0.9600444838
Kurtosis8.167241698
Mean20.64740891
Median Absolute Deviation (MAD)3.945
Skewness2.914780478
Sum10199.82
Variance392.9287718
MonotocityNot monotonic
2020-09-12T14:31:56.177766image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
100102.0%
 
12.8730.6%
 
8.5830.6%
 
12.1820.4%
 
11.8920.4%
 
13.3520.4%
 
19.8220.4%
 
10.8720.4%
 
8.9720.4%
 
10.1920.4%
 
Other values (433)46493.9%
 
ValueCountFrequency (%) 
5.3710.2%
 
5.6610.2%
 
5.7210.2%
 
5.9410.2%
 
5.9510.2%
 
ValueCountFrequency (%) 
100102.0%
 
99.9410.2%
 
99.7610.2%
 
99.6410.2%
 
99.5710.2%
 

Interactions

2020-09-12T14:30:58.038705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:30:58.618700image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:30:58.936644image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:30:59.258511image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:30:59.574194image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:30:59.877109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:00.202844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:00.501749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:00.797715image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:01.109210image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:01.413446image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:01.735625image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:02.051681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:02.371031image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:02.672003image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:03.094633image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:03.407534image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:03.706546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:04.004312image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:04.319229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:04.629698image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:04.928646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:05.240354image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:05.541544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:05.838043image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:06.162508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:06.469393image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:06.781370image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:07.091553image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:07.418817image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:07.722331image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:08.031741image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:08.352955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:08.680066image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:08.988242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:09.308390image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:09.612028image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:09.926598image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:10.228792image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:10.551829image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:10.861134image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:11.165539image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:11.631686image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:11.952290image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:12.259653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:12.572479image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:12.896503image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:13.198696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:13.503426image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:13.804643image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:14.098717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:14.405093image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:14.719382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:15.019062image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:15.345297image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:15.670828image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:15.961844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:16.257080image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:16.570389image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:16.882261image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:17.177290image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:17.474527image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:17.785694image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:18.092844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:18.423441image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:18.740389image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:19.044078image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:19.363636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:19.667185image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:20.099420image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:20.398852image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:20.729448image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:21.024740image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:21.326604image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:21.646616image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:21.969166image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:22.271195image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:22.567864image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:22.891316image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:23.188067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:23.481634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:23.788671image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:24.088865image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:24.405622image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:24.706424image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:25.018916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:25.313532image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:25.615602image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:25.921029image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:26.214026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:26.507328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:26.840055image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:27.148306image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:27.445046image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:27.737264image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:28.057855image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:28.348473image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:28.787252image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:29.117926image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:29.425972image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:29.721842image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:30.033832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:30.385079image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:30.771875image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:31.112965image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:31.452914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:31.788202image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:32.136199image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:32.450796image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:32.928517image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:33.251652image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:33.596713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:33.910412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:34.235547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:34.554304image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:34.886325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:35.194106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:35.495098image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:35.804550image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:36.151270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:36.447000image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:36.752185image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:37.062229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:37.392190image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:37.859911image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:38.201788image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:38.512348image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:38.821511image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:39.125516image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:39.450711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:39.764620image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:40.083010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:40.402339image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:40.697488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:41.009022image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:41.322036image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:41.613304image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:41.906102image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:42.200117image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:42.522415image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:42.811933image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:43.101526image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:43.405680image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:43.723309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-09-12T14:31:56.502400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-12T14:31:57.094329image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-12T14:31:57.573580image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-12T14:31:58.056903image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-09-12T14:31:44.314227image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-12T14:31:44.855452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

SymbolSeriesDatePrev CloseOpen PriceHigh PriceLow PriceLast PriceClose PriceAverage PriceTotal Traded QuantityTurnoverNo. of TradesDeliverable Qty% Dly Qt to Traded Qty
0RCOMEQ15-May-201732.4532.5032.6031.7032.0032.0031.9971263162.279973e+0811883190804226.77
1RCOMEQ16-May-201732.0032.0532.3531.8532.0032.1032.1272748812.336877e+089976221191030.40
2RCOMEQ17-May-201732.1032.1032.3031.7031.9031.9531.9762451141.996360e+088838139981522.41
3RCOMEQ18-May-201731.9531.6031.9031.0031.0531.0531.34111899383.506776e+0817051362917532.43
4RCOMEQ19-May-201731.0531.2031.4530.2530.5530.5530.80118987803.664547e+0818065330952927.81
5RCOMEQ22-May-201730.5530.7530.8029.7029.9029.8030.08101505353.053526e+0819810346392934.13
6RCOMEQ23-May-201729.8029.7029.8027.2027.9528.0028.16259308207.300927e+0842741547297321.11
7RCOMEQ24-May-201728.0028.0028.2025.3025.9025.9026.34462436161.218051e+09604421221161426.41
8RCOMEQ25-May-201725.9025.9026.5525.1526.2025.9525.90282574487.318248e+0835034687271324.32
9RCOMEQ26-May-201725.9526.0026.3025.3525.9525.7525.93140248303.636344e+0820142464358033.11

Last rows

SymbolSeriesDatePrev CloseOpen PriceHigh PriceLow PriceLast PriceClose PriceAverage PriceTotal Traded QuantityTurnoverNo. of TradesDeliverable Qty% Dly Qt to Traded Qty
484RCOMEQ26-Apr-20192.001.902.051.901.901.901.92661332541.268149e+08143463417562451.68
485RCOMEQ30-Apr-20191.901.851.851.851.851.851.85228614264.229364e+0743722233642597.70
486RCOMEQ02-May-20191.851.901.901.901.901.901.90166880443.170728e+0739331618240896.97
487RCOMEQ03-May-20191.901.951.951.951.951.951.9526174945.104113e+0615272617394100.00
488RCOMEQ06-May-20191.952.002.002.002.002.002.0023670254.734050e+0614082367025100.00
489RCOMEQ07-May-20192.002.002.102.002.102.102.06641992121.325275e+0891033467747454.02
490RCOMEQ08-May-20192.102.102.202.102.202.202.18274556365.972756e+0742081495783454.48
491RCOMEQ09-May-20192.202.302.302.302.302.302.3078541261.806449e+072385771177398.19
492RCOMEQ10-May-20192.302.402.402.402.402.402.4035845928.603021e+0615603584592100.00
493RCOMEQ13-May-20192.402.452.502.302.302.302.40423295161.014503e+0895412630957562.15